Jan 09, 2018
Thank you very much for making this course available on Coursera, I cannot agree more the knowledge of Mr Venkat. This is a great way to help people to get started with Google Machine Learning.
Sep 08, 2018
A very good course on TensorFlow, ML and Google MLE on GCP.\n\nThe Labs are self contained and the problems proposed are very challenging. I learned a lot on this course.\n\nThank you!
By Scott Molitor•
Dec 14, 2018
Detailed and incredibly useful for DE's
By rodrigo orellana•
Dec 10, 2018
if you read this before taking this course, believe me you will need PATIENCE!
By Miguel Antonio Cortes Nannig•
Dec 07, 2018
By Anjan Singha•
Dec 04, 2018
It was really very informative session. After going through this session, I feel there are lots of things to learn and I will. Thanks Lak for the wonderful course material.
By Claudio Ikeda•
Dec 03, 2018
a powerful tool in a practical and simple way to empower data engineers with this tool possibilities
By Daniele Scarano•
Nov 27, 2018
Lot of information are contained in this short course. It's a perfect approach to ML even if it's a bit confusing if you don't have previous experience. Great course learned lot of things.
By Atanu Ghosh•
Nov 26, 2018
Quite knowledgeable. It touches all the aspects of Model building along with the Google Cloud implementation . Learned a lot out of it. Thanks a lot to Lak and team for the course
By Alvin Pradeep E•
Nov 19, 2018
overall good. the lab should have more cpu. for small mbs data dataflow,cloud ml took 15 -20 min .ALso we can combine jypter labs to single instead of repeating the process for multiple small labs.It takes lot of tine to launch data lab
By Jose Fernando Caballero Pastor•
Nov 19, 2018
El curso es correcto, demasiado denso, para acabar solo arañando la superficie de lo que es Machine Learning. Los laboratorios ha sido pésimo.
Nov 18, 2018
Great instructor and everything works well. I just wish, as I did for the rest of the course, there was more hands on coding rather than just a review and clicking next. Better for learning purposes. But, with a Python foundation you should be able to understand what's going on by cloning github.